Center for Analytical Finance University of California, Santa Cruz

Working Paper No. 42

FOMC Sentiment Extraction and its Transmission

to Financial Markets

Raul Cruz Tadle

Department of Economics, UC Santa Cruz

May 26, 2017

Abstract

I use Automated Content Analysis, adopted from computational linguistics and political science,

to derive sentiments acquired from Federal Open Market Committee (FOMC) meeting documents.

I assign an index to the minutes in order to determine if the sentiments obtained from the

information therein can be classified as hawkish (analogous to improving economic conditions and

stronger inflationary pressures) or dovish (related to deteriorating economic outlook and subdued

price changes). I compare the sentiments of the discussions in the minutes to the sentiments of

information in corresponding FOMC statements released immediately after the meetings and

calculate the surprise component of the relative sentiments. I then evaluate how this news shock in

the minutes impacts broad equity and real estate investment trust indices, as well as the exchange

rate valuation of different world currencies against the U.S. Dollar. My findings indicate that

financial assets respond to the minutes based on the type of news shock they contain and that

financial markets react more significantly during the FOMC's date-based policy guidance period.

Keywords: Financial Market, Asset Price, Central Bank, Fed, FOMC, Monetary Policy,

Exchange Rates

JEL codes: E43, E44, E52, E58, F31, F42

About CAFIN

The Center for Analytical Finance (CAFIN) includes a global network of researchers whose aim is

to produce cutting edge research with practical applications in the area of finance and financial

markets. CAFIN focuses primarily on three critical areas:

• Market Design

• Systemic Risk

• Financial Access

Seed funding for CAFIN has been provided by Dean Sheldon Kamieniecki of the Division of Social

Sciences at the University of California, Santa Cruz.

1 Introduction

The types of public releases provided by the Federal Reserve (Fed) have evolved over time.

Not only has the Fed publicized the quantitative change of its policy tools, especially the federal

funds target rate, but it has also released qualitative information, such as its meeting documents,

which detail the reasoning behind its policies. In particular, there are two sets of meeting

documents whose releases are widely anticipated due to the regular and timely information they

provide. The first is the meeting statements, which are released at 2 PM Eastern Time on the last

day of scheduled FOMC meeting slots. These provide a brief summary of the information used

in the decision-making process of the Federal Open Market Committee (FOMC), the main Fed

committee that determines monetary policy in the United States. They also contain assessments

of the risks on employment and inflation, the two measures that constitute the FOMC dual

mandate.1 The second document type consists of the meeting minutes, which are released three

weeks after the meetings. The minutes correspond to the information from the same meeting,

but contain more extensive nuances and details about the FOMC’s information.2

The most direct and immediate impact of these Fed releases are assessed through the

reactions of financial assets. These reactions are closely examined because they could trigger

significant changes in the real economy.3 Due to the influence that these markets have on the

economy, the Fed has placed much emphasis on them, as is documented in FOMC discussions.

Financial markets, on the other hand, scrutinize the information in FOMC meeting documents

because they discuss policy decisions as well as economic forecasts, specifically those that FOMC

members monitor closely when deciding the appropriate monetary policy to implement.4 These

forecasts, which signal future policy, have significant influence on expectations about economic

fundamentals. Given that asset pricing depends not only on current but also on the likely

evolution of economic indicators - in addition to interest rates - that react largely to monetary

policy, financial market prices move significantly following changes in both the current and

expected future policies.

1See Bernanke et al. (2004) for more discussions regarding the ‘balance-of-risk’ component of the FOMCstatements.

2Although the minutes are not verbatim records like the transcripts, they provide more detailed summaries ofthe discussions and projections than are those found in the statements.

3Bernanke and Kuttner (2005) add further insight about the importance of financial market reactions anddescribe some of the channels through which these reactions could influence the real economy.

4Romer and Romer (2000) find that the FOMC has superior information that is not publicly accessible.

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Although FOMC documents matter to financial markets, asset valuations significantly change

after the release of the documents only if the information they contain are new or surprising to

market participants. Previous work has found that unexpected information in FOMC statements

cause significant financial market reactions.5 Since the minutes correspond to the same meetings

as the statements, if the statements fully represent the details of the meetings, then the financial

markets should not be reacting to the minutes releases. This is because the information in the

statements are already accounted for by financial markets and the minutes are, in this case,

redundant. On the other hand, if there is new information in the minutes, then asset prices

would adjust to account for this news. My current work thus examines whether considerable

discrepancies in the corresponding FOMC documents are present and evaluates how they affect

asset prices.

To examine the information component from the two meeting documents and to evaluate

whether significant differences exist between them, I use Automated Content Analysis. I extract

information sentiments that proxy for two sets of information: the FOMC economic outlook

based on the current and projected state of the economy and the committee’s overall policy tilt in

the short and medium term. Following the literature on FOMC document sentiment extraction,

I classify the sentiments as hawkish if they relate to more optimistic economic forecasts and/or

larger foreseen inflationary pressures. Hawkish sentiments indicate a higher propensity for the

FOMC to implement contractionary monetary policy. On the other hand, I categorize the

information that portrays a more negative outlook and/or more subdued inflation indicators

as dovish, which conveys a higher chance of the FOMC conducting expansionary monetary

policy. Using these sentiment categories, I derive the unexpected sentiment component of the

minutes. I then evaluate how the surprise sentiment, which I denote as the news shock, affects

financial market variables, such as U.S. and emerging market equity indices and foreign exchange

valuations against the dollar.

My results indicate that unexpectedly more hawkish (dovish) minutes tend to cause negative

(positive) reactions on financial markets, particularly on equity and Real Estate Investment Trust

(REIT) indices. These imply that markets respond negatively (positively) to the perceived higher

likelihood of contractionary (expansionary) monetary policy. The magnitude of the results is

subdued, however, given that asset prices may have two opposing reactions. Hawkish sentiments,

5Some of the papers discussing the impact of FOMC statements on financial markets are Gurkaynak et al.(2005), Lucca and Trebbi (2011), and Rosa (2011).

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particularly those that emphasize improving economic conditions, have positive impacts on

financial markets.6 In contrast, these same sentiments also signal impending rate increases,

which have negative effects on asset prices. Therefore, given these two opposite reactions to the

same type of sentiment, the observed results are muted.

To circumvent these counteracting effects, I analyze financial market reactions during the

FOMC’s date-based policy guidance period. This form of guidance removed the uncertainty

about the policy path by committing to a specified policy up to a given date in the future.7,8

Therefore, information in the minutes released during this date-based commitment period serves

primarily as a source of economic and inflation outlook. Once this more specific policy guidance

is implemented, I observe that the aforementioned market indices experience positive (negative)

and significant reactions following a more hawkish (dovish) news shock.

My work thus affirms that FOMC minutes releases have significant impacts on financial

market prices. More importantly, this research is the first to evaluate how asset prices vary

based on the type of surprise information sentiments of the minutes. My findings contribute to

the understanding of how FOMC documents may be used to affect the economy in addition to

conventional policy releases, such as announcements of changes in the Fed Funds rate target.

They also give insight into how documents can be better tailored to conduct more effective

central bank transparency.9 Together with the conventional monetary policy tools, information

contained in FOMC documents have a significant impact that can potentially be used to achieve

the Fed mandates of maximum employment and price stability, while keeping interest rates in

moderate levels in the medium term.

The rest of my paper is structured as follows. Section 2 elaborates on the motivation for

the current work. Section 3 discusses relevant literature. Section 4 describes the method used

to calculate sentiments in FOMC documents. Section 5 describes the financial data. It also

conducts an event study to show stylized facts regarding the changes in the standard deviation

of asset prices following the releases of the minutes. Section 6 examines the effects of the news

component of sentiments in the minutes on various financial market indicators, while section 7

6The impact of hawkish sentiments driven mostly by high inflationary pressures are ambiguous.7In particular, in the August 2011 meeting, the FOMC announced that it will be maintaining its then ongoing

expansionary policy until mid-2013. This was later extended to mid-2015.8See Swanson and Williams (2014) for further discussions on the date-based forward guidance.9See Walsh (2007) for a theoretical discussion of optimal central bank dissemination of information. Also see

Blinder et al. (2008) for a survey covering the evolution of central bank communication and transparency.

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evaluates the robustness of the results. Finally, section 8 presents concluding remarks.

2 Motivation

Most of the empirical literature examining the effectiveness of monetary policy decisions

evaluate the impact of policy implementation, as reflected by changes in the federal funds rate

target, on treasuries of varying maturities, stock markets, and foreign exchange rates.10 However,

the FOMC also provides qualitative information using regularly released documents. This set

of qualitative information not only includes discussions regarding the type of policy that is

implemented; it also incorporates economic projections available to the FOMC during their

meetings as well as indications about the path that monetary policy may take based on such

projections. The evaluation of these FOMC documents is important to further incorporate more

of the information that market participants consider when adjusting the pricing of their assets.

There are two documents, in particular, that are released regularly and which the markets

consider to be available in a timely manner. Briefly after each regularly-scheduled meeting that

occur about every six weeks, a statement is released (around 2 PM Eastern Time). This set

of documents provides a succinct explanation behind the chosen monetary policy. Three weeks

afterwards, the meeting minutes are distributed. These documents relay more information that

are available to the FOMC members during the meeting, such as the committee members’

own projections and the Board of Governor’s staff projections. They also incorporate details

about the then-current measures of various macroeconomic indicators, including unemployment,

housing starts, and inflation.

Several papers have evaluated the qualitative information contained in FOMC documents.11

These research work have focused mainly on the statements, not only because they are released

in conjunction with the monetary policy ruling, but also because they are easier to evaluate

manually.12 Nonetheless, some work assess the reactions of financial markets to the FOMC

minutes releases, but they are limited since they focus on changes in the standard deviation

of financial asset returns immediately following the releases of minutes and do not indicate

10See Kuttner (2001) and Faust et al. (2007) for some examples of this type of evaluation.11Examples of research papers evaluating the qualitative information in FOMC documents include Rosa (2011),

Cannon (2015), Stekler and Symington (2016), and Jegadeesh and Wu (2017).12These statements are about one page long, on average, while the minutes are roughly eight to ten pages in

length.

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how such reactions vary based on the perceived policy signals and economic outlook from the

information. In addition, no research to date has simultaneously examined the qualitative

information content of both the statements and minutes while also evaluating the impact of

the unexpected information sentiments of the minutes. Therefore, analyzing the information

content of the two sets of documents in a more transparent and consistent method bridges this

gap in the literature and helps determine how markets respond to information differences.

To conduct this study, I use the Dictionary Method of Automated Content Analysis – a

method that is widely used in both computational linguistics and political science – in order to

evaluate the sentiments of the information in the minutes.13 I conduct the same analysis on the

contents of the statements and calculate the relative sentiments of the two documents. From

this, I obtain the surprise component of the relative sentiments and examine the type of asset

pricing adjustments that they cause.

3 Literature

A significant amount of literature has examined the effect of FOMC releases in terms of the

reactions of financial markets. This is because monetary policy is intended to try to affect the

real economy indirectly through the changes in financial assets.14 Given the immediate response

of financial markets to the surprise changes in policy rates, their reactions have become the

measure by which to evaluate the impact of monetary policy.

In the mid-1990’s when the FOMC steered towards greater transparency, the FOMC began

to announce explicit changes to the fed funds target rate shortly after the meetings. These

releases had a significant influence on the expectations formation of financial market participants.

Documenting the differences in the target rate and its future rate counterpart, Nosal et al.

(2001) and Carlson et al. (2006) reiterated that the difference between the two, especially during

recessionary periods, were smaller after the implementation of explicit announcements of the

federal funds target. They argued that even with this simple amount of guidance, the financial

markets improved in forming expectations regarding the target rate.

Ehrmann and Fratzscher (2007) adds to the discussion regarding expectation formation by

13See Grimmer and Stewart (2013) for more discussions regarding the Dictionary Method and AutomatedContent Analysis, in general.

14Bernanke and Kuttner (2005) offers additional discussions regarding the indirect effect of monetary policythrough financial markets.

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reiterating that through alterations in overnight interest rates, the FOMC indirectly affects the

long-run interest rate. This logic follows from the idea that the interest rate in the future is

simply reflected by a series of short-term interest rates and that any changes in expectations

of future short-term interest rates can very well affect long-term interest rates. Not only can

FOMC actions have repercussions in the short-term, they may also be able to affect the long-run

fundamentals. Therefore, when a central bank is able to communicate well and be transparent

about its actions, expectations about the future can be anchored by the policy path that it maps

out.

The impact of funds rate target movements is not limited to interest rates. Bernanke and

Kuttner (2005) extends Kuttner’s seminal work using fed funds futures and evaluates the impact

of the unexpected component of the target rate changes on equity prices. They find that the

unexpected component cause a very large reaction from equity markets.

Given that FOMC releases occur on scheduled dates, anticipation may also play a role in how

financial markets react to these releases. Lucca and Moench (2015) document a drift in equity

prices beginning a couple of days prior to the announcements of FOMC decisions. Since this

drift does not seem to exist for other macroeconomic releases, they argue that this movement

in equity prices not only demonstrates the amount of attention that the equity markets place

on FOMC policy, but also of how the expectations of policy, itself, may affect equity market

movements.

In addition, financial market indicators not only react to announced alterations in the policy

target, but may also change depending on the discussions that FOMC members hold regarding

monetary policy. To emphasize this point, Aizenman et al. (2016) examine FOMC member

speeches during the ‘tapering tantrum’ period. They find that during this period, tapering

news, particularly those relayed by the chairmen, have large and adverse impact on exchange

rates. The observed effect is largest among those countries with a combination of low external

debt, current account surpluses, and large amounts of international reserves.

There are also research evaluating the impact of FOMC document releases on financial

markets.15 Kiley (2014) analyzes the movements in long-term rates that is resulting from FOMC

15The assessments in these papers are structured around major changes in FOMC documents. For instance, backin May 1999, the FOMC began to release more elaborate and systematically-released statements that describethe rationale for the target as well as the policy tilt. This was augmented just a few months afterwards byincorporating ”balance of risks.” Another major alteration occurred on the meeting minutes back in December2004 when the release of the minutes, which was previously set at roughly six weeks after each meeting, was

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statements using the first-principal component of short-term interest rates. He then evaluates

how they impact equity markets before and during the Zero Lower Bound (ZLB) period of the

fed funds rate. He finds that equity prices are much more sensitive to the changes in long-term

rates prior to 2009 compared to the period during the ZLB. Additionally, Gurkaynak et al.

(2005) evaluate the impact of FOMC statements on financial markets. They observe that much

of the reactions of the markets are due to the information about the future path of policy as

conveyed by the statements.

Rosa (2013), on the other hand, explains that the minutes, which are issued three weeks

after the meetings, also have significant effects on financial markets as reflected by large spikes

in the volatility of asset prices. This increase in volatility does not last longer than the end

of the trading day, thereby demonstrating that the financial market is able to adjust its asset

valuations shortly after the minutes are released. Extending on the findings of Rosa (2013),

Apergis (2015) also disentangles the impact of FOMC minutes releases on the prices of several

assets using GARCH volatility modeling. He finds that the reaction of the mean and volatility

of these asset prices are more subdued during the financial crisis.16

Indeed, financial market reactions may depend on the type of information the documents

contain and a nascent literature has turned to using Content Analysis, a common method used

in other academic fields, in order to evaluate the qualitative information of the documents. The

methods implemented by this strand of research can be thought of as being two types: heuristic

and automated. The heuristic implementation simply occurs when an individual or group of

individuals manually analyzes the content of the documents and quantifies the information

content. An example of the heuristic implementation is conducted by Rosa (2011). He examines

the impact of monetary policy statements - in conjunction with policy changes - on exchange

rates. He finds that the statements have large and significant effects on the valuation of the U.S.

dollar against other global currencies.

A criticism regarding heuristic Content Analysis is that it is more sensitive to the bias of the

evaluator(s). The manner of implementing this method may be ambiguous and, at times, even

inconsistent. Lucca and Trebbi (2011) shows a case in point as they conduct both a heuristic

expedited to three weeks. This earlier release schedule assured that the information in the minutes are moretimely and would therefore preserve their significance.

16Jubinski and Tomljanovich (2013) also finds significant reaction on volatility but not on the mean of the assetprices. However, they only utilized data between 2006–2007 while Apergis (2015) uses data from 2005-2011.

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and an automated evaluation of the statements. They find that the heuristic evaluation does

not provide consistent examination especially when compared to the findings of their method

conducted algorithmically. Hence, much of Content Analysis, especially the one used in the

current project, is conducted in an automated manner. This type of content analysis allows

me to be as consistent and transparent as possible in analyzing the contents of the FOMC

documents.17

Moreover, the FOMC documents not only include the rationale for implemented policy but

also contain what is known as forward guidance (FG), or indications of potential changes in

the policy rule in the near future. Campbell et al. (2012) indicate that FOMC FG has two

components: Odyssean and Delphic. Odyssean FG is the component that specifies what policy

will be taken in the future. It binds the monetary policy body to implement it or risk losing

credibility. On the other hand, Delphic FG indicates the forecasts of future fundamentals that

the FOMC uses in their discussions. They imply the nature of policy the FOMC is more likely

to implement.

The sentiment indices I obtain in my current work are analogous measures of the Delphic

FG expressed in FOMC documents. The indices are calculated based on discussions regarding

forecasts of economic fundamentals as well as inflation levels and can therefore proxy for the

perceived amount of risks imposed on the FOMC’s dual mandate. These sentiments hint at the

evolution of policy that the FOMC is considering based on the projections the members observe.

On the other hand, the date-based FG period, the time when the FOMC commits to

implementing a specified policy for a pre-determined set of dates, is analogous to the Odyssean

FG since it indicates a promise to maintain policy for a specified time. As this FG is implemented,

the sentiments in the FOMC documents, particularly in the minutes, proxies for the insight and

beliefs of FOMC members regarding inflation and the economy. They focus much less on policy

expectations and place more emphasis on shaping the public’s beliefs about economic outlook.

Campbell et al. (2016) add to the discussion by empirically and theoretically analyzing the

impact of the two FG components. They use fed funds futures and examine how the release of

the FOMC’s private information about future economic indicators, as represented by Greenbook

Forecasts, may be influencing the expectations for the future fed funds target rate. They find

17See Antweiler and Frank (2004), Boukus and Rosenberg (2006), Apel and Grimaldi (2014), Grimmer andStewart (2013), El-Shagi and Jung (2015), and Stekler and Symington (2016) for additional examples anddiscussions of the use of Content Analysis.

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significant responses of the expectations for the policy rate and also observe that most of the

reaction can be attributed to Delphic FG.

However, the work that Campbell et al. (2016) present does not directly use the qualitative

information incorporated in the FOMC statements. Their empirical analysis simply uses the

Greenbook forecasts in lieu of the qualitative information. Although these Greenbook forecasts

may be affecting the decisions of the FOMC, they do not fully represent the information used in

the meetings given that the committee members also incorporate other information, such as their

own forecasts, when making monetary policy decisions.18 The information in the documents are

more representative of those used by the FOMC members when they discuss policy given that

they contain a larger scope of details about meeting materials and discussions. Hence, examining

the qualitative information of these documents provide better and more timely understanding of

the FG that the FOMC members are trying to convey. Analyzing these documents will then lead

to a better measure of the type of reaction that financial markets have based on the qualitative

information they acquire.

4 Sentiment Analysis

The FOMC began to release the meeting minutes three weeks after the scheduled meetings,

beginning with the December 14, 2004 meeting. The FOMC members offer the reasoning, within

those minutes themselves, why they expedited the release from the previous schedule of releasing

the documents after six weeks as follows:

Participants noted that the minutes contained a more complete and nuanced explanation

of the reasons for the Committee’s decisions and view of the risks to the outlook than

was possible in the post-meeting announcement, and their earlier release would help

markets interpret economic developments and predict the course of interest rates.

When they were released after a six-week lag, the minutes were seen by some market participants

as offering information that was already ’stale’ or not that useful. Hence, the expedition of the

minutes release not only highlights the relevance of these documents but also enables the FOMC,

18Moreover, the public is also able to consult the information in FOMC documents when making expectationsabout the funds target rate given that they are available much sooner than the Greenbook forecasts, which arepublicly released after a five year lag. Hence, the actual documents are more consistent with the FG that financialmarket participants are able to observe.

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themselves, to more effectively guide market expectations.

The impact of the minutes depend on the information that they provide. Since the contents

of the minutes that I analyze in my work are qualitative - or expressed in words - instead of

quantitative, I adopt a method that could quantitatively assess their information. To extract a

measure to proxy for the information in the documents, I utilize Automated Content Analysis.

This type of analysis has had many uses in various fields, such as in political science, computational

linguistics, sociology, and even economics, and is typically used to obtain information in documents,

blogs, speeches, and social media posts, especially tweets.19 There are numerous methods

associated with this type of analysis and the choice depends on the structure of the classification.

4.1 Automated Content Analysis: Dictionary Method

In order to create a measure incorporating both the economic outlook as well as the overall

monetary policy inclinations of the FOMC, I conduct the Dictionary Method of Automated

Content Analysis.20 It allows me to determine not which topics are important, but rather, how

they are conveyed in the FOMC documents. Using the method, I can assess how the details

in the meeting documents portray the general outlook regarding unemployment, production,

inflation, and other economic indicators. It requires, however, that I provide categories with

which to classify the information in the documents.

To be consistent with the ongoing literature examining FOMC documents, I utilize two

pre–specified categorization for the documents I am evaluating. These categories are what I

refer to as sentiments. They describe the general outlook that FOMC members have regarding

both economic and inflation conditions as well as signal FOMC policy inclination. They are

classified as either hawkish or dovish. Hawkish sentiments are those that indicate improving

economic outlook and inflationary pressures. Therefore, hawkish sentiments indirectly signal a

higher likelihood of monetary policy tightening. On the other hand, dovish sentiments tend to

emphasize more details regarding deteriorating economic conditions and subdued price changes,

thereby hinting at a higher probability that monetary policy loosening would occur.

The Dictionary Method also requires lists of keywords and related terms, or what are termed

as ‘dictionaries’. The terms in the ‘dictionaries’ are relevant to the categories that are examined

19See Gorodnichenko and Shapiro (2007) for an application of the method on inflation targeting.20Both of these sets of information are included in the same measure. They are combined because disentangling

them is difficult since they are not independent of one another.

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and symbolize relevant topics that the FOMC emphasizes in their discussions.21

Using the Dictionary Method allows me to implement my document information analysis

in an automated fashion using Python’s Natural Language Processing capability. With the use

of the automated method, I can consistently evaluate the documents as well as enable future

research to replicate the same methods that I have taken.22 Therefore, my results can easily be

validated.

In order to categorize the minutes, I first collect copies of the minutes from the FOMC website

beginning from the one corresponding to the December 14, 2004 meeting up to the minutes of

the December 15 - 16, 2015 meeting. While examining the minutes and the statements, I have

compiled a set of keywords that pertain to inflation and other indicators of economic outlook.

The list of keywords are shown in Table 1.23 The keywords are very similar to those found in

Jegadeesh and Wu (2017), who utilized Latent Dirichlet Allocation (LDA), a different Automated

Content Analysis method, in order to analyze the topic variations in the minutes.24

To evaluate the FOMC minutes (and their corresponding statements in the next subsection),

I have also created lists of positive and negative terms that are found in the FOMC documents.

The lists are shown in Table 2. Their polarization depends on their use in the English lexicon

and are comparable to the terms used by Lucca and Trebbi (2011). I use these sets of terms to

calculate the overall sentiments of the documents.

The keywords used are then categorized as hawkish or dovish. The hawkish keywords are

those terms that, if associated with positive terms, depict hawkish sentiments. The hawkish

keywords, on the other hand, represent dovish sentiments if they are linked to negative terms.

21The keywords and categories chosen are consistent with previous work on FOMC documents. Since thecategories are pre-established and are well-documented in the literature, the analysis is not subject to overfitting,which is a problem that needs to be addressed when using other methods.

22As Lucca and Trebbi (2011) demonstrate, evaluations of FOMC statements may be prone to the bias ofthe individuals examining the documents. They argue that the heuristic approach is inferior to the automatedapproach that they utilize in their analysis since the automated method significantly reduces the amount ofsubjectivity to which their study is exposed.

23There are noticeably more hawkish than dovish keywords. This does not affect the analysis given that thesekeywords reflect the word choice that the FOMC documents use to convey information. Moreover, the sentimentsare not based solely on these keywords but are determined depending on the context under which these keywordsare used.

24The difference with the approach used by Jegadeesh and Wu (2017) is that they use LDA to distinguish topicsthat are emphasized in the minutes. They do not, however, account for the information similar to those in thestatements. They also do not determine the cumulative price changes of different financial market indicators tothe surprise information sentiments. Finally, they examine the reactions of the S&P 500 from the moment theminutes are released up to 15 minutes ahead and therefore do not account for the traders who read the entireminutes as well as those who may have withheld their trading shortly before the minutes release.

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In contrast, dovish keywords are those terms which portray dovish sentiments when they are

connected to positive terms while these same keywords depict hawkish sentiments if associated

with negative terms.

To illustrate my keyword categorization, I note that the keyword ‘prices’ is a hawkish keyword

and the term ‘higher’ is a positive term. When taken together, ‘higher prices’ depict a relatively

more hawkish sentiment. This is because with higher observed prices, the risk of inflation

increases, thereby causing the Fed to lean towards setting contractionary monetary policies

- actions that could fend off inflationary risks but which may negatively impact employment

levels. On the other hand, ‘higher unemployment’, in which ‘unemployment’ is a dovish keyword,

portrays a relatively more dovish sentiment. This is because with higher unemployment, the

FOMC observes weakness in the economy and hence will be more likely to implement (or

maintain) expansionary monetary policy - policy decisions meant to reduce unemployment but

which could trigger increases in inflation.

After creating my sets of keywords and polarized terms, I implement my initial sentiment

scoring at the sentence level. I separate out the documents into sentences and take out the

punctuations and capitalizations.25 Eliminating the sentences without any of the keywords

follows since these sentences do not portray any significant information regarding the sentiment

of the documents. Hence, for each document d, I am left with nd sentences.26

Each sentence is given a score as follows. Denoting sentence s with keyword type k as sentd,k,

the sentiment score of the sentence depends on the number of positive terms, p, relative to the

number of negative terms, n. Thus, the sentiment score of sentd,k, score(sentd,k), is given by

score(sentd,k) =

1, if k = hawk & p > n

−1, if k = hawk & p < n

−1, if k = dove & p > n

1, if k = dove & p < n

0, otherwise

(1)

25Some researchers also remove commonly occurring ‘stop words’ and afterwards, stem the words - or stripthe words to their roots - before conducting their analysis. I abstain away from doing these changes since thesemodifications could potentially change the meaning and context of the sentences that I am analyzing.

26Sentences that contain both hawkish and dovish keywords are scored the same as those with only hawkishkeywords given that these sentences portray sentiments in the same manner as sentences with only hawkish keys.

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Sentence scoring examples following this scheme are shown in Table 3. Example Sentence 1

incorporates hawk keywords and more positive than negative terms. Therefore, it is given a

sentence score of ‘+1’, which indicates a hawkish sentiment for that particular sentence. On

the other hand, Example Sentence 3 also includes a hawk keyword but has more negative terms

than positive. Based on this evaluation, it is given a sentence score of ‘-1’, which implies that

the sentence conveys a dovish sentiment. As illustrated, the sentence sentiment scores are not

only determined by the type of keywords they contain, but are also dependent on the relative

number of positive and negative terms they have.

Moreover, I also account for the negation terms when scoring these sentences.27 When a

positive term is in the proximity (that is, if they occur after three words or less) of a negation

term, then it is counted as a negative term. On the other hand, a negative term is counted as

positive if it immediately follows a negation term.

Finally, I calculate the document sentiment score by aggregating the sentence scores and

dividing them by the number of sentences with keywords as shown by

index(d) = 100 ∗ 1

nd

∑score(sd,k) (2)

This calculation controls for the number of relevant sentences in each document, thereby accounting

for potential increases in length of the FOMC documents over time.28 Accounting for the method

used to calculate the sentiment index, figure 3 shows that a higher index value implies that the

document is more hawkish while the index with a lower value implies that the document is more

dovish.

4.2 Executing the Automated Content Analysis on the Minutes

As an initial step, I remove the section in the minutes that consists of the whole statement

released shortly after the meetings since market participants have already gained access to them

three weeks prior. Therefore, any information that this section contains will already have been

accounted for by the market. Next, I conduct the Automated Content Analysis to each of the

FOMC meeting minutes. The sentiment scores of the minutes are shown in figure 4, in which

the gray bar depicts the Great Recession.

27The negation terms used in this analysis are ‘fail’, ‘less’, ‘never’, ‘no’, ‘not’, ‘opposed’, and ‘unlikely’.28Changing nd to be the overall number of sentences per document does not alter the implications of the results.

13

Prior to the economic downturn, the discussions during the FOMC meetings, as reflected

by the minutes, tended to be more hawkish. The more hawkish sentiments reflected the more

optimistic FOMC view about the economy and the inflationary concerns prevalent during this

period. As the recession approached, the minutes sentiment score declined. Given that inflation

concerns, as reflected by discussions in the documents, were still large in the beginning of

the recession, the more dovish sentiments implied that the FOMC began to observe signs of

impending economic weakness. The perceived weakness continued to worsen until its trough in

December 2008, when the FOMC decided to hold the fed funds rate at a range with zero as its

effective lower bound value.29

In the meetings following the official end of the recession in June 2009, more hawkish

sentiments emerged, partially due to the fact that the FOMC also began to monitor the financial

markets, specifically the equity markets, more closely at this time.30 In conjunction with

the gradual reduction in unemployment, the discussions became more hawkish preceding the

economic turbulence that occurred in Europe in 2011. During the height of the European

sovereign debt crisis, there was a short dip in the hawkishness of the FOMC. Only minimal

repurcussions on the US economy occurred after economic tensions in Europe were observed.

Therefore, the sentiments of the information in the FOMC discussions rebounded and continued

to fluctuate above zero.

To further evaluate the indices corresponding to the minutes, I examine whether the observed

trends are statistically significant. I use the nonparametric computational method from Zeileis

et al. (2003) in order to evaluate whether structural breaks in the sentiment indices occur. The

results, consistent with earlier discussions, are shown in Figure 5. Following the BIC criterion,

I find statistical significance for two structural breaks in the series, denoted by the dotted lines

in the figure. The resulting periods coincide with the hawkish sentiment during the Great

Moderation, the pessimism during the economic downturn, and the more subtle hawkishness

coinciding with the recovery period.

29Stekler and Symington (2016) find that it was not until after the collapse of Lehman Brothers in October2008 when the FOMC fully realized the severity of the economic downturn.

30During this period, the equity markets experienced a significant increase and discussions about them havecontributed significantly to the hawkish sentiments of the minutes.

14

4.3 Comparison of the Sentiments in the Minutes and the Statements

The information in the statements is also crucial in assessing how the financial markets

respond to the minutes. A comparison of the indices will not only demonstrate similar the

trends between the two sets of documents, but will also determine whether large deviations

between them occur. Hence, I conduct the same methodology on the statements and compare

them to those calculated for the minutes. The corresponding sentiment indices are shown in

Figure 6.

Comparing the indices of the statements to those of the minutes, I found large deviations

at different periods. The statements tended to be more hawkish than the minutes before the

recession hit. As the Great Recession occurred, the sentiments of the statements fluctuated

from being more dovish at the beginning of the downturn to becoming more hawkish than the

minutes when some signs of weak recovery were observed. Following the collapse of Lehman

Brothers, the sentiments in the statements dropped significantly (even lower than the minutes

sentiments). When the recession ended, the sentiments in the statements began to inch closer

to the sentiments in the minutes. After 2012, the two sentiment indices have began to move

together more closely.31

In general, the information sentiments in the statements are more volatile than those of the

minutes. This is because the discussions in the statements are concentrated on rationalizing

the policy decisions as well as the implied monetary policy path. The details on the minutes,

on the other hand, include conflicting views of the FOMC members. This inclusion of mixed

perspectives about prices and the overall state of the economy results to more subdued sentiments

from the minutes.

5 Data and Stylized Facts

I begin my empirical analysis by determining whether financial market indicators react to

the releases of the minutes. To do this examination, I conduct an event study using intraday

data. The use of high frequency data is common in the literature examining the effects of

31There are two possible explanations for this. The first is that the FOMC have began to more closely matchthe information in the minutes and the statements. The second, and perhaps more likely explanation, is thatthe increasing length of the statements enabled it to procure additional information that would have only beenavailable in the minutes. Thus, the movements of the sentiment indices based on the two documents reflect thischange.

15

macroeconomic announcements and news on financial markets. This is because it helps isolate

the impact of the examined release from the effects of other macroeconomic information. Therefore,

the use of the intraday data in my current work attribute any reactions of the markets to releases

of the minutes.

5.1 Data

The data I am using consists of the NYSE trade quotes of Exchange-Traded Funds (ETFs)

in the period ranging from December 1, 2004 to April 30, 2014. The ETFs are equities that

closely follow a scaled value of various financial market indices. The ETFs used in this analysis

are the SPY, VNQ, EEM, and EWJ. The SPY, the ETF for the U.S. equity market, closely

tracks the S&P 500 stock index as it is priced based on the same basket of stocks that the S&P

500 is calculated with. Bond et al. (2012) highlight the “... potential real effects of financial

markets that stem from the informational role of market prices.” Bernanke and Kuttner (2005)

add that evaluating the general impact on equities is crucial given that equity pricing reflects

some of the changes in both the cost of capital and the private portfolio valuation. Therefore,

equity valuation is a way to examine the effect of new information from FOMC discussions on

financial markets, and also serve as an indirect measure of the potential impact of monetary

policy documents on the real economy.

The VNQ, on the other hand, is the ETF for the Dow Jones Real Estate Investment Trust

(REIT) Index. It closely follows that MSCI US REIT Index, which represents about 99% of the

US equity REITs.32 Therefore, it proxies for the valuation of real estate stocks. It also acts as

a measure of commercial and residential development activities since the MSCI US REIT Index

represents large companies that develop and manage apartment complexes and business offices.

Given that surprise changes in monetary policy affects real estate valuation, it is plausible that

discussions about these policies also trigger some pricing changes.33 It is thus insightful to

examine how document surprises affect the REITs.

EEM is the ETF that tracks the exposure of MSCI large and mid-sized emerging market

companies.34 The higher the value of EEM is, the larger the weighted investment measure

32It does not, however, include mortgage REIT.33See Iacoviello and Minetti (2008) and Bredin et al. (2007) for discussions about the impact of monetary policy

on REITs and on the housing market.34There are 23 emerging markets involved. 42% of the companies followed by the index are from countries that

16

on the companies from emerging markets are. In addition, EWJ tracks the large and mid-

sized companies that are from Japan. It represents roughly 85% of the Japanese stock market.

These two ETF’s measure part of the the international effects of the sentiments of the FOMC

documents. Given that policy rate decisions and discussions about U.S. economic outlook of

the FOMC have confounding effects on global capital and trade flows, the financial markets in

other countries may also be responding to the FOMC document releases.35 Hence, incorporating

these ETF’s are relevant in order to determine how influential the surprise sentiments from the

document information are to foreign equity markets.

I also complement my ETF data with high frequency foreign exchange (forex) data acquired

from Tick Data.36 I utilize the level 1 Best Bid and Offer (BBO) pricing of four of the largest

world currencies against the dollar, namely U.S. Dollar to Japanese Yen, U.S. Dollar to Swiss

Franc, Great Britain Pound to U.S. Dollar, and Euro to U.S. Dollar. This set of data ranges

from May 7, 2008 to Jan. 12, 2016.

For my analysis, I aggregate the data so that I have high frequency data for equally spaced

time intervals. More specifically, I take the last trade price of each five minute interval of the

ETF data. As for the forex data, I utilize the midpoint of the last tick of each five minute

interval to proxy for the forex value.37

5.2 Preliminary Empirical Methodology using Five-Minute Log Returns

To examine whether the volatility spikes following the release of the minutes, I first calculate

the log returns of each financial market variable using the equation

rT,τ = 100 ∗ log(

PT,τPT−1,τ

)

where PT,τ is the effective asset price level at time period T on day τ . Hence, for the ETF data,

PT,τ is the last trade price of time interval T . As for the forex data, PT,τ is the midpoint of the

last tick in time interval T .

constitute BRICS, i.e. Brazil, Russia, India, China, and South Africa.35See Bruno and Shin (2015) for discussions about the effect of monetary policy on cross-border bank capital

flows.36Rosa (2011) finds significant reactions of exchange rates against the U.S. Dollar following monetary policy

and meeting statement surprises.37Using the median quote of each time interval does not qualitatively affect the results.

17

Financial data that is examined at tick-by-tick frequency is highly influenced by outliers.

These values do not represent the activities occurring in the financial markets since they are

typically caused by erroneous placements of bid and ask prices.38 To address potential outliers

in the data, I remove the returns with values that are greater than the 95th percentile or lower

than the 5th percentile of the corresponding time period. The remaining data, therefore, more

accurately represents ongoing market activity.

Afterwards, I separate the returns on the release days of the minutes from the set of days

in which minutes nor statements are released. The type of day is denoted by DS. Therefore,

rT,τ,DS is the five-minute change in the asset price at the given time period T on day τ of day set

DS. The statement days are excluded from this analysis since many papers have already cited

significant price movements of various financial market variables on these days. This follows

from the fact that monetary policy changes, together with a brief rationalization for such a

change – the statements – are announced on this set of days.39

5.3 Volatility Comparisons

Calculating the standard deviation of rT,τ,DS for each five minute interval T determines

whether large reactions in financial markets occur shortly after the release of the minutes. The

calculation is conducted for each set of days using the formula

σT,DS =

√∑ΞDSτ=1 (rT,τ,DS − rT,DS)2

ΞDS

where rT,DS is the mean of rT,τ,DS for time interval T of all ΞDS days in day set DS.

Following Rosa (2013), I compare the standard deviations of days with releases of the minutes

to those with no FOMC statement or minutes releases. The comparisons are shown in Figures 1

and 2. I find that prior to the release of the minutes at 2PM Eastern Time, the standard

deviation of the five-minute returns of the ETFs tended to move about the same during the

release days of the minutes and the non-release days.

Interestingly, as the minutes are released (depicted by the red vertical line), the volatility of

the five minute returns spikes for all of the financial market variables. This large spike in the

38See Brownlees and Gallo (2006) for more discussions about outliers.39The omission of the days with statement releases does not qualitatively affect the results.

18

standard deviations is not observed on days with no FOMC document releases. The volatility

on release days fluctuates then slowly converges to the corresponding standard deviation of

nonrelease days.40

This finding demonstrates that the financial markets react to the releases of the minutes,

implying that the information they contain also matter to market participants. More importantly,

such a reaction may depend on the type of ‘new’ information in the minutes. Hence, I derive a

measure of the new information in this set of documents before assessing the direction of asset

price movements on days when the documents are released.

6 Empirical Methodology and Results

6.1 Assessing the Relative Sentiments of the Minutes

The rise in the volatility of the financial market variables following minutes releases suggests

that there is some significant informational value in the minutes beyond those already presented

in the statements. This information from the minutes must be differentiated from the statements

in order to isolate the information that cause market participants to reevaluate their positions.

To address this idea, I calculate the difference of the two sentiment indices, or what I refer to

as the relative sentiment (RSt), as

RSt = Mt − St (3)

where Mt is the sentiment score of the minutes and St is the sentiment score of its statement

counterpart.

One concern about this measure is that the sentiment scores of the statements and minutes,

even though they were calculated by the exact same method, may not necessarily be comparable.

Hence, in order to have a more consistent measure of the sentiment scores, I take ZMt and ZSt ,

the standardized values of the sentiment scores of the minutes and statements, respectively, and

40To examine the robustness of these stylized facts, sets of days are randomly selected to determine whethersuch movements on minutes release days can be observed in various sets of non-release days. This is not an issue,however, given that no such reactions are observed in the placebo sets examined.

19

calculate the relative sentiment, RZt, using the equation41

RZt = ZMt − ZSt (4)

Figure 7 shows the values ofRSt andRZt over time. Since the two series have similar movements,

I take equation 4 as the value of relative sentiment of the minutes compared to the statements.42

6.2 Expected Component of the Sentiments

Rosa (2011) points out that macroeconomic fundamentals change gradually and that due

to this consistency, the discussions between meetings resemble one another. Consequently, the

statements and minutes from one meeting to the next tend to portray very similar contents,

thereby making the differences in the information and the respective sentiments to be persistent.43

The information conveyed by the previous relative sentiment that has persisted to the current

set of statements and minutes are already accounted for in the pricing of financial assets and

will therefore not cause any significant reaction from markets. However, there is a part of the

relative sentiments that is unexpected and thus cause the markets to react. To extract this ‘news’

component from the rest of the content of the minutes, I first calculate the expected value of RZt

using the Maximum Likelihood Estimation (MLE) method, which provides a parameterization

of the expected component.

Similar to the construction used in Rosa (2011), I use the MLE method and employ the

forecasting regression specification

RZt = γ0 + γRZt−1RZt−1 + γZStZSt + ξt (5)

where I include the statement sentiment index score since market participants have already

observed the discussions in the statements and its information may affect the expectations of

the participants.

The results are given in Table 5. I find that both the RZt−1 and ZSt are significant predictors

of RZt. Hence, I confirm that the past measure of relative sentiments has a significant amount

41In order to standardize the sentiment scores, I subtract the mean and divide by the standard deviation.42Table 4 shows the summary statistics of RZt.43BIC and AIC model selection confirms the consistency of the relative sentiments for one period.

20

of predictive power even after accounting for the information in the statements.

6.3 Surprise Sentiments of the Information in the Minutes

In order to obtain the surprise sentiments, I difference out the expected component. To do

this, I take the results from section 6.2 and compute the expected value of the relative sentiment

as shown by

Et−1(RZt) = γRZt−1RZt−1

= 0.368 ∗RZt−1

for each FOMC minutes.44 Using this measure of expected relative sentiments, the unexpected

component of the relative sentiments, which I denote as the news shock NSt, is given by

NSt = RZt − Et−1(RZt)

= RZt − 0.368 ∗RZt−1

The descriptive statistics for NSt are shown in Table 6.45 Panel A shows that over the course of

the time period under consideration, the average change in relative hawkishness is negative, but

close to zero in absolute value terms. Panel B and Panel C, moreover, provide the descriptive

statistics for the time periods covered by data type.46

6.4 Basic Regression Specification and Results

To examine the impact of the news shock on financial markets, I use multiple regression

analysis. This enables me to measure the effect of the surprise sentiments on the financial

market variables while controlling for time-specific variations as well as perceptions about the

overall equity market riskiness that may have influence over asset returns. The basic regression

44Including the weighted value of the statement score as part of Et−1(RZt) does not qualitatively change theresults.

45The use of an alternative measure of NSt is explored in section 7.6.46The data for the ETFs cover more of the Great Moderation Period while the data for the ForEx pairs covers

a greater span of the ZLB period.

21

specification is given by

rft = α+ βNSNSt + βV IXV IXt + βY Y + ht

In this specification, rft = 100 ∗ log(P4:00,t

P1:50,t

)is the log percentage return of market indicator f

from 1:50 PM, ten minutes prior to the minutes release, to 4:00, the end of trading day t. This

measure determines the cumulative effect of the information in the minutes on the ETFs and

ForEx pairs examined for the rest of the trading day.47 Hence, rV NQt is the log percentage return

of VNQ, the ETF of REIT, from 1:50 PM to 4:00 PM of day t. V IXt is the log percentage

change of the adjusted closing price of VIX on day t.48,49 It serves as a measure of the perceived

financial market riskiness and therefore have been found to have significant impacts on the

changes in asset prices. Furthermore, Y is a vector of year dummy variables used to implement

year fixed effects while ht is the error term of the specification.

The basic regression results are given by Table 7. Panel A gives the results for ETFs while

Panel B provides the results for the different ForEx pairs with the U.S. dollar. I observe that

the estimated news shock coefficients for the ETFs, aside for the EWJ, have a negative sign

while results for the exchange rates, except for USD to JPY, suggest that the dollar appreciates

following surprisingly more hawkish sentiments.

6.5 Main Regression Specification and Results

The previous results are statistically insignificant. This is perhaps due to the combination

of direct and indirect effects of monetary policy discussions. Although the documents signal

monetary policy through the information regarding economic and inflation outlook, the inclusion

of this information may also trigger revisions to the economic outlook of market participants.

Surprisingly more hawkish minutes may increase the perceived likelihood of future contractionary

policy. Since financial asset valuation depends on expected future rates, the signals about more

likely contractionary policy could have adverse effects on asset prices. The positive news shock

47I focus on these financial variables since they have economically larger results than the expectations for thefederal funds rate. This is because much of the time period under consideration cover the zero lower bound periodof the federal funds rate. The impact on the expectations for the federal funds rate, measured using the fed fundsfutures, are examined in more detail in section 7.1.

48VIX is the implied riskiness of the financial market and is measured using the annualized value of the expectedstandard deviation change in the S&P 500 stock index in the next 30 days.

49Data for the adjusted closing price of VIX is obtained from Yahoo Finance on Feb. 11, 2016.

22

of the minutes, on the other hand, may also be positively reshaping the perceptions of future

economic conditions if driven by positive economic outlook. Consequently, financial market

valuation rises.50 The combination of these contradictory effects may cause the financial markets

to have weaker and statistically insignificant reactions to surprise sentiments in the minutes.

One way to account for these counteracting effects is by holding one of them as fixed. Such

an event occurred in the midst of the recovery period following the Great Recession when the

FOMC decided to change its FG implementation to utilize more definitive FG methods. In

particular, Swanson and Williams (2014) find that August 9, 2011 corresponds to the time when

the FOMC first used date-based FG, which replaced the announcement of keeping the policy

rate at their near-zero levels “... for an extended period” with “... at least through mid-2013.”

This change in FG effectively removed the uncertainty regarding the implied path of monetary

policy, at least for a specified amount of time, by indicating that the FOMC will be holding the

prevailing expansionary policy until the middle of 2013.51

Consistent with the findings of Swanson and Williams (2014), I incorporate the FOMC

meeting date - which serves as the beginning of the date-based FG - to evaluate whether

the unexpected information in the minutes have a different impact after the date-based FG

is implemented. To do so, I include in the regression analysis the indicator variable l2011, which

takes a value of 1 for the period after August 8, 2011, and 0 otherwise. The augmented regression

specification measuring the impact on different market variables is given by

rft = α+ βNSNSt + βl2011∗NSl2011 ∗NSt + βV IXV IXt + βl2011 l2011 + βY Y + φt

where l2011 ∗ NSt is the interaction term that represents the additional reaction to the news

shock during the implementation of the date-based FG. In the specification, φt stands for the

error term. The results for the ETF’s are reported in Table 8.52 I find negative (and statistically

significant for VNQ) coefficient estimates for the news shock. These results indicate that before

the date-based FG was implemented, an unexpected one standard deviation increase in the

50The impact of the perceived future economic conditions is ambiguous when discussions about inflation aredriving a significant portion of the news shock.

51The “... at least through mid-2013” phrase was later modified to “... at least through 2014” and “... at leastthrough mid-2015” in the January 2012 and September 2012 meetings, respectively. The phrase was then revisedto “... a considerable time after the asset purchase program ends...” in the December 2012 meeting. None of thesechanges qualitatively affected the results.

52Changing the cutoff date to 2012 gives very similar results.

23

relative sentiments of the minutes caused a decline in the MSCI REIT Index of approximately

51.5 basis points, after accounting for the daily changes in the VIX as well as year-specific

variations. Therefore, these results suggest that the equity and REIT indices react negatively

when the relative sentiments of the minutes is unexpectedly larger compared to the relative

sentiments of the information from the previous minutes.

Moreover, the coefficient estimates for l2011 ∗NSt are positive and highly significant for SPY

and VNQ. To interpret, considering the daily change in the adjusted closing price of VIX as well

as year-specific changes, these results indicate that an unexpected standard deviation increase

in the news shock during the date-based FG period leads to an increase of the S&P 500 index by

46.9 basis points. These results imply that as the date-based FG is implemented, the subsequent

observed reactions are not only larger, in absolute value terms, but are also the opposite of how

the financial markets tended to react to the news in the minutes.

It is insightful to compare my results to the reactions following surprise changes in the

fed funds rate. I find that during the date-based FG, the S&P 500 broad equity stock index

experiences a 19 basis point increase following an unexpected one standard deviation rise in the

relative sentiments of the minutes. As an indirect comparison, Bernanke and Kuttner (2005)

highlight that broad stock indices rose by 1% point following a surprise 25 basis point cut in

the federal funds rate. Hence, the news shock from the minutes have economically significant

impacts on U.S. broad stock indices. In addition, the magnitude of the reactions of the REIT

index is large. Following an unexpected one standard rise in the relative sentiments, the equity

REIT index rises about 47 basis points. This is significant relative to the results obtained by

Bredin et al. (2007). They find that an 82 basis point increase in REIT index a day after

observing a 25 basis point surprise drop in the fed funds rate.

The findings extend beyond the indices tied to domestic markets. The reactions of EEM (the

ETF for Emerging Market Equities) and EWJ (the ETF for Japanese Equities) react similarly

as the SPY. Hence, these values lend support to the notion that not only do domestic financial

markets react to the surprising information content of FOMC documents, but so do foreign

financial markets.

In addition, Table 9 also shows the results for various foreign currencies against the U.S.

dollar. Except against the Japanese Yen, the coefficient estimates for NSt indicate that before

the date-based FG, there are some statistically insignificant appreciation of the US Dollar

24

following an unexpected increase in the relative sentiments. As the date-based FG occurred,

the Japanese Yen has a statistically significant (at the 5 % level) and positive increase in its

valuation against the U.S. dollar following positive news shocks while the rest of the exchange

rates do not significantly react to the unexpected information sentiments of the minutes.53

6.6 Discussions about the Results

The FOMC uses the statements and minutes to share the information they have during

their meetings and further guide financial market expectations about monetary policy. An

indirect effect of these documents is that they also cause market participants to reevaluate their

forecasts regarding economic indicators, based on both the perceived policy path as well as on

how the FOMC believes the economy and inflation will change. Therefore, discussions regarding

improving economic prospects may be conceived as partly stimulatory, given that they cause

optimism in the markets. On the other hand, they also hint at a higher probability of monetary

policy tightening, which has the opposite effect. Therefore, coefficient estimates for NSt are

statistically insignificant for most of the market indicators examined, although they still suggest

that unexpected hawkish (dovish) sentiments during this relatively more uncertain policy period

cause several of the financial market indicators to contract (expand).

More importantly, the interaction term in the regression circumvents the counteracting effects

of the sentiments. This term represents the change in the impact of the news shock during

the date-based FG period. Since the date-based FG period is a time with policy certainty, the

sentiments obtained from the information in the FOMC documents simply reflect the perceptions

about the economy and inflation and do not provide any significant signals regarding changes

in policy. Therefore, the information from the documents do not result to muted impacts on

financial markets.54

Additionally, as shown in Figure 8, low perceived inflation risk coincides with the timing of

the date-based FG period. In the figure, the inflation rate from the previous year is well below

the target of 2%. As a result, the surprisingly hawkish (dovishness) information sentiments of

53The extensions presented in the next section focuses on the results of the ETFs since they are mostly significantwhile the relevant coefficient estimates for the forex pairs are mostly insignificant.

54Considering the changes in the FOMC forward guidance when the Evans Rule was implemented does not alterthe findings that the news shocks driven by optimistic economic outlook have positive impacts on asset prices. Imaintain the division of the time period as the period prior and during the date-based forward-guidance periodto simplify the discussions. This does not alter the qualitative results given that the Evans Rule simply acts asan alternative policy commitment mechanism of the FOMC.

25

the minutes during this period represent projected improvements (deterioration) of economic

fundamentals and therefore leads to increased optimism (pessimism) in financial markets, both

domestic and abroad.

7 Robustness and Extensions

7.1 Determining whether the Sentiments affect the Fed Funds Futures

The discussions in the minutes related to the evaluations of economic and inflationary

factors for the short and medium term determine the sentiment of the FOMC documents. Such

information causes reactions in the financial markets, as portrayed by movements in stock price

indices, real estate investments, and to a lesser degree, exchange rates. The reactions of many

of these markets are significant during the date-based FG. Although the sentiments measure the

indirect signals about the likely evolution of monetary policy, expectations about the target rate

may also react to them since the FOMC utilizes the information from the economic and inflation

forecasts they gather in order to implement monetary policy. It is plausible that despite the delay

in the release of the minutes, the sentiments from these documents also cause further reaction

to the expectations about the policy rate in addition to those triggered by the statements. To

evaluate this possibility, I use federal funds rate futures, which proxy for market expectations

regarding future federal funds rates for different time periods.

To conduct the analysis, I use the main regression from section 6.5 but take the daily log

percentage change of the funds futures as the dependent variable. I examine the fed funds

futures for different horizons, namely the one-month, three-month, six-month, and twelve-month

horizons. The results are shown in Table 10. I find that for fed funds futures with short horizons

(less than six months), the news shock does not have a statistically significant impact. For the

six-month and twelve-month funds futures, there are significant coefficient estimates for NSt

and l2011 ∗NSt.

Interestingly, the coefficient estimate for NSt is positive while the coefficient estimate for

the interaction term is negative. These imply that before the date-based FG was implemented,

the surprise component of the relative sentiments in the minutes led to a small increase in the

price of the futures contract (lowered the expected funds rate) corresponding to a few quarters

ahead.55 Once the date-based FG was placed in the FOMC releases, minutes with positive news

26

shocks had the opposite effect since the futures contract price declined (which consequently

increased the expected funds rate).

It is worth noting that the economic value of these results are very small, especially during the

date-based FG period, when a surprise one-standard deviation increase in the relative sentiments

of the minutes led to a decrease of 0.6 basis points for the six month ahead horizon and a fall

of about 1.5 basis points for the twelve month horizon. These futures rate movements are

much smaller than the 25 basis point increments that occur when actual policy is implemented.

Therefore, these results simply reflect the perceived higher likelihood of future monetary policy

changes and does not signal definitive changes in the policy rate.

7.2 Distinguishing between the Effects of Hawkish and Dovish Sentiments

The length of the minutes enables them to demonstrate relatively more extensive information

that includes mixed signals about the economy. The committee members have their own and, at

times, varying projections for these indicators and thus, their beliefs about the manner in which

the employment and inflation rates will change in the near future may vary significantly. The

minutes then act as a source of qualitative information, especially regarding FOMC member

disagreements that, if naively combined without further evaluation, may not necessarily portray

an accurate depiction of how sentiments affect financial markets.

A particular method that can be used to assess how varying types of beliefs about the

economy as well as inflation could affect financial markets is differentiating between sentences

that hold hawkish sentiments compared to those that portray dovish sentiments. As discussed

earlier, hawkish sentiments emerge from sentences that portray improving or stronger economic

conditions as well as high inflation whereas dovish sentiments are a result of discussions regarding

weakening economic variables and small projected price changes, typically in the form of low

inflation rates.

To evaluate the hawkish and dovish sentiments of each document, I follow a similar approach

as the sentence sentiment scoring shown in equation 1. The main difference is that I calculate the

aggregate number of hawkish and dovish scores separately before dividing each sentiment type

score by the total number of sentences with keywords in each document. This calculation gives

55Determining why the price of longer-horizon fed funds futures rise following positive news shock is beyondthe scope of this paper.

27

Hawk(d) and Dove(d), the overall concentration of hawkish and dovish sentences, respectively.

Next, I calculate the standardized value of Hawk(d) as ZHt,d and the corresponding z-score

of Dove(d) as ZDt,d. As before, this standardization ensures that the sentiment measures are

comparable. Afterwards, I calculate the difference in the corresponding z-scores of the individual

sentiment types between the minutes and their respective statements. The resulting relative

sentiments are denoted as RZHt and RZDt .

I also need to obtain the expected component of the relative sentiments. To do so, I use the

equation

RZgt = γ0 + γRZgt−1RZgt−1 + γZg

t,SZgt,S + ξt

where g stands for either H for hawk or D for dove. Table 11 gives the results. Similar to

previous findings, the persistence of the relative concentration of hawk and dove sentiments are

high and statistically significant. In particular, the coefficient estimate for RZHt−1 is 0.271 while

the estimate for RZDt−1 is 0.419.

I then calculate NSHt and NSDt , the unexpected component of the relative standardized

concentration of hawkish and dovish sentences, as shown by

NSHt = RZHt − Et−1(RZHt ) = RZHt − 0.271 ∗RZHt−1

NSDt = RZDt − Et−1(RZDt ) = RZDt − 0.419 ∗RZDt−1

Table 12 also shows the descriptive statistics of NSHt and NSDt . The regression specification

in this analysis is very similar to the main regression specification in section 6.5. The modified

regression used is given by

rfa,b,t = ν + βNSHNSHt + βNSDNSDt + βl2011∗NSH l2011 ∗NSHt

+βl2011∗NSD l2011 ∗NSDt + βl2011 l2011 + βV IXV IXt + βY Y + ψt

The results are indicated in Table 13. I observe that NSHt and NSDt have negative and positive

coefficients, respectively. On the other hand, l2011 ∗ NSHt and l2011 ∗ NSDt have the opposite

signs as their indicator counterparts. These findings are consistent with the earlier findings

that the concentration of hawkish sentiments before late 2011 caused a decline in the financial

28

markets while those that occur beginning in August 9, 2011 tend to have positive impacts on

the markets.

In addition, the unexpected relative concentration of hawkish sentiments, in particular, has

a large and statistically significant effect during the date-based FG. This further supports the

claim that during this period, hawkish sentiments, and not the decline in the amount of dovish

discussions, are responsible for the positive reactions of financial markets.

7.3 Examining the Reactions of Financial Markets during the ZLB period

To determine whether the impact of the sentiments are attenuated by the occurrence of the

ZLB, I use the regression specification

rft = α+ βV IXV IXt + βl2011 l2011 + βZLB∗NSZLB ∗NSt + βZLBZLB + βY Y + φt

The difference between this specification and that used in section 6.5 is that I replaced NSt and

l2011 ∗NSt with the indicator variable ZLB and its interaction term with NSt.

My results are given in table 14. For each ETF, there are two columns of results. The

first column corresponds to the results from the original regression specification. On the other

hand, the second indicates the results while using the ZLB indicator and its interaction term.

Interestingly, when I examine the results with the ZLB variable, I find positive and significant

coefficient estimates for EEM and EWJ, the equity index measures for emerging countries and

Japan, respectively, while the estimates for SPY and VNQ are positive but insignificant. These

results suggest that the foreign equity markets respond to the surprise component of the relative

hawkishness acquired from minutes information whereas domestic markets have a significant

response only during the date-based FG.

Given that the large companies from Brazil, Russia, India, China, and South Africa (BRICS)

constitute much of those included in the EEM, a significant portion of the companies covered

by EEM and EWJ are linked directly to trading with the U.S. Therefore, the importance of

FOMC document information regarding the state of U.S. economic recovery may help explain

the significant reactions of international equity markets on surprise relative sentiments. Still,

exchange rates do not have significant reactions to the unexpected component of the sentiments.

Therefore, the trade link may only partially explain the reactions of the international equity

29

indices to the news shock during the ZLB period.

7.4 Evaluating the Robustness of the Forward Guidance Date Cutoff

Examining the sensitivity of the date cutoff is crucial given that much of the impact from

the date-based FG may be due to the first few meetings immediately after it was implemented.

It is then possible that after 2011, the impact of the date-based FG is insignificant. To assess

this possibility, I use the modified regression specification

rft = α+ βNSNSt + βpost2011∗NSpost2011 ∗NSt + βV IXV IXt + βpost2011post2011 + βY Y + φt

where I simply replace the variable l2011 and its corresponding interaction term with post2011

and its interaction variable.56 post2011 is an indicator variable that takes a value of 1 for dates

after 2011, and 0 otherwise.

My results are reported in Table 15. I report the original findings using the main specification

as well as those arising from the use of post2011. I find that the results are insensitive to the

change in the timing of the date-based FG. Hence, the implications regarding the impact of the

unexpected hawkishness of the minutes hold.

7.5 Examining the Returns on Minutes Release Days

Earlier findings evaluated the impact on the returns from the surprise sentiment obtained

from the minutes. It is, however, also informative to examine the differences in the returns

solely on minutes release days since these provide a much clearer depiction of the impact of

the surprise component of the minutes. This is because asset price movements on days without

minutes releases may still occur due to other factors. Isolating these effects could give more

clarity about the impact of the news shock.

Table 16 gives the results using the main regression specification. For each of the ETF data,

there are two columns of results. The first indicates the previous findings when all trading

days are considered whereas the second column includes the results when examining only the

minutes release days. I observe that the results for the interaction term l2011 ∗ NSt are larger

for SPY, VNQ, and EEM when only considering the minutes release days. They are also highly

56Changing this indicator variable to another indicator for September 2011 does not qualitatively alter theresults.

30

significant. In contrast, EWJ has a smaller coefficient estimate for the interaction term when

regressing the returns during minutes release days only but still is statistically insignificant.

In addition, the R2 for each of the regressions examining the release days are much higher

than their counterparts examining all days. This is consistent with the notion that much of the

variability of returns on release days largely depends on the variables examined, especially the

surprise component of such documents. Hence, much of the activity for the rest of the trading

day following the release of the minutes are based on their content.

7.6 Using the Residuals as the Unexpected Component of the Minutes

The unexpected component of the minutes is measured by first taking the value of the

expected component through MLE estimation and then subtracting it from the relative zscore.

However, referring back to Eq. 5, the error term, ξt, is the component of the relative z-score that

is not explained by either its lag value and the z-score of the statement sentiment. Therefore,

ξt can be used as the alternative measure of the unexpected component.

Using ξt, the regression specification is given by

rft = α+ βξξt + βl2011∗ξl2011 ∗ ξt + βV IXV IXt + βl2011 l2011 + βY Y + φt

Table 17 gives the results when evaluating returns on all days as well as on the release days

of the minutes. Similar to earlier findings, there are statistically significant and qualitatively

similar results for the interaction term l2011 ∗ ξt for SPY, VNQ, and EEM. Furthermore, the

value of the coefficient estimate for the interaction term is larger in magnitude when evaluating

only minutes release days compared to the examination incorporating all of the days.

7.7 Bootstrap Standard Errors

One additional issue regarding the use of estimated values as independent variables is that

the standard errors, computed in the multiple regression analysis, may be imprecise. This is

because standard errors are calculations of mistakes of predicted values based on the observed

independent variables. If the independent variables used are estimated, then they may not

perfectly represent the actual values of their underlying variables.

A workaround adjustment for the additional mistakes from using estimated variables is using

31

bootstrap standard errors. The analysis with bootstrap standard errors emerged from Monte

Carlo simulation and repeatedly estimates the model using sampling with replacement such

that the standard errors are adjusted to better reflect the mistakes that the model specification

creates. Focusing on the returns on the release days of the minutes, the results are included in

Table 18.

For each of the evaluated ETF’s, there are two columns of findings. The first column

represents the results originally obtained for days with minutes releases. Hence, this set of

results incorporate the original robust standard errors. The second column of each ETF has the

same coefficient estimate but with larger standard errors obtained using the bootstrap method.

Despite the larger standard errors, the coefficient estimates for the interaction term l2011 ∗NSt

are statistically significant at the 10% level for the SPY and EEM and at the 5% level for the

VNQ. Therefore, the results are robust to the larger standard errors obtained from the bootstrap

method.57

8 Concluding Remarks

Communication through documents has increased its relevance to recent monetary policy-

making as central banks from different parts of the world have explored unconventional ways to

stimulate their economies. These documents are intended to affect the expectations formation

about the path that monetary policy will take. Based on the results of my work, the documents

can be used for more than just implementing communication strategies that affect expectations

about policy. They can also be used to guide the outlook regarding inflation and the economy

as well as to impact financial markets to try to influence the real economy.

As it moved away from a regime of secrecy, the FOMC has increased both the frequency

and length of its communication. In doing so, it has increased the amount of information it

releases to the public, especially in terms of the discussions and forecasts released with the

meeting documents. However, the FOMC’s perspective on these markets, in conjunction with

the overall state of employment and price stability may also be self-fulfilling, especially if not

conveyed with much thought and consistency. The FOMC must continue to be careful about

the way in which they discuss their policy-making process, especially as the committee members

57The findings using the error term ξt as the unexpected component are also robust to the use of bootstrapstandard errors.

32

search for a balance between sustaining the recovery of the U.S. economy and preparing for the

future downturns to come.

My current project creates a testable measure of how to systematically evaluate the FOMC

meeting documents, particularly by examining their impact on financial markets in the U.S. and

in other countries. However, the current analysis on financial markets reactions only observe the

immediate effects of these documents. More work must be done to determine just how influential

these meeting documents are to the global economy, as well as to understand if and how the

benefits from such communication are distributed widely.

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36

Figure 1: Standard Deviations of ETFs

.04

.08

.12

.16

Sta

ndar

d D

evia

tion

12:00 1:00 2:00 3:00 4:00Time (PM)

SPY (S&P 500)

.04

.08

.12

.16

Sta

ndar

d D

evia

tion

12:00 1:00 2:00 3:00 4:00Time (PM)

VNQ (MSCI U.S. REIT).0

4.0

8.1

2.1

6S

tand

ard

Dev

iatio

n

12:00 1:00 2:00 3:00 4:00Time (PM)

EEM (EME Equities)

.04

.08

.12

.16

Sta

ndar

d D

evia

tion

12:00 1:00 2:00 3:00 4:00Time (PM)

EWJ (MSCI Japan Index)

FOMC Minutes Days Days w/o Minutes or Statements

Figure 2: Standard Deviations of ForEx Pairs

.015

.02

.025

.03

.035

Sta

ndar

d D

evia

tion

12:00 1:00 2:00 3:00 4:00Time (PM)

USD-JPY

.015

.02

.025

.03

.035

Sta

ndar

d D

evia

tion

12:00 1:00 2:00 3:00 4:00Time (PM)

USD-CHF

.015

.02

.025

.03

.035

Sta

ndar

d D

evia

tion

12:00 1:00 2:00 3:00 4:00Time (PM)

GBP-USD

.015

.02

.025

.03

.035

Sta

ndar

d D

evia

tion

12:00 1:00 2:00 3:00 4:00Time (PM)

EURO-USD

FOMC Minutes Days Days w/o Minutes or Statements

37

Figure 3: Interpreting the Sentiment Scores

Figure 4: Sentiment Scores of the FOMC Minutes

-80

-40

040

80S

core

jan05jan05 jan05 jan07jan05 jan07 jan05 jan07 jan09jan05 jan07 jan09 jan05 jan07 jan09 jan11jan05 jan07 jan09 jan11 jan05 jan07 jan09 jan11 jan13jan05 jan07 jan09 jan11 jan13 jan05 jan07 jan09 jan11 jan13 jan15jan05 jan07 jan09 jan11 jan13 jan15

Date

38

Figure 5: Sentiment Index of FOMC Minutes: Structural Breaks

Date

Sco

re

jan05 jan07 jan09 jan11 jan13 jan15

−80

−40

040

80

Figure 6: Sentiment Scores of the Minutes and Statements

-80

-40

040

80S

core

jan05jan05 jan05 jan07jan05 jan07 jan05 jan07 jan09jan05 jan07 jan09 jan05 jan07 jan09 jan11jan05 jan07 jan09 jan11 jan05 jan07 jan09 jan11 jan13jan05 jan07 jan09 jan11 jan13 jan05 jan07 jan09 jan11 jan13 jan15jan05 jan07 jan09 jan11 jan13 jan15

Date

FOMC Minutes FOMC Statements

39

Figure 7: Relative Sentiment Score vs. Relative Z-score

-80

-40

040

80S

core

jan05jan05 jan05 jan07jan05 jan07 jan05 jan07 jan09jan05 jan07 jan09 jan05 jan07 jan09 jan11jan05 jan07 jan09 jan11 jan05 jan07 jan09 jan11 jan13jan05 jan07 jan09 jan11 jan13 jan05 jan07 jan09 jan11 jan13 jan15jan05 jan07 jan09 jan11 jan13 jan15

Date

Non-standardized Standardized

Note: The standardized measure is scaled by 20 in this figure.

Figure 8: Trimmed Mean PCE Inflation

01

23

4In

flatio

n

jan00jan00 jan00 jan02jan00 jan02 jan00 jan02 jan04jan00 jan02 jan04 jan00 jan02 jan04 jan06jan00 jan02 jan04 jan06 jan00 jan02 jan04 jan06 jan08jan00 jan02 jan04 jan06 jan08 jan00 jan02 jan04 jan06 jan08 jan10jan00 jan02 jan04 jan06 jan08 jan10 jan00 jan02 jan04 jan06 jan08 jan10 jan12jan00 jan02 jan04 jan06 jan08 jan10 jan12 jan00 jan02 jan04 jan06 jan08 jan10 jan12 jan14jan00 jan02 jan04 jan06 jan08 jan10 jan12 jan14 jan00 jan02 jan04 jan06 jan08 jan10 jan12 jan14 jan16

Date

40

Table 1: Keywords by Type

Hawkish Terms

business businesses demand economic

economy employment energy equities

equity expansion financial growth

housing income indicators inflation

inflationary investment investments labor

manufacturing outlook output price

prices production recovery resource

securities slack spending target

toll wage wages

Dovish Terms

accommodation devastation

downturn recession

unemployment

41

Table 2: Polarized Terms

Positive Terms

abating* accelerated add advance advanced

augmented balanced better bolsters boom

booming boost boosted eased elevated

elevating expand expanding expansionary extend

extended fast faster firmer gains

growing heightened high higher improved

improvement improving increase increased increases

increasing more raise rapid rebounded

recovering rise risen rising robust

rose significant solid sooner spike

spikes spiking stable strength strengthen

strengthened strengthens strong stronger supportive

up upside upswing uptick

Negative Terms

adverse back below constrained contract

contracting contraction cooling correction dampen

damping decelerated decline declined declines

declining decrease decreases decreasing deepening

depressed deteriorated deterioration diminished disappointing

dislocation disruptions down downbeat downside

drop dropping ebbed erosion fade

faded fading fall fallen falling

fell insufficient less limit low

lower moderated moderating moderation reduce

reduced reduction reluctant removed restrain

restrained restraining restraint resumption reversed

slack slow slowed slower slowing

slowly sluggish sluggishness slumped soft

softened softening stimulate strained strains

stress subdued tragic turmoil underutilization

volatile vulnerable wary weak weakened

weaker weakness

* The term 'abating' is labeled as positive since it is used to describe the deterioration in labor market conditions.

42

Table 3: Examples of Sentence Scoring

Example Sentence 1:

according to survey information expectations of near term inflation⏟ ℎ𝑎𝑤𝑘 𝑘𝑒𝑦

picked upด𝑝𝑜𝑠

in march

consistent with the increase⏟ 𝑝𝑜𝑠

in energy prices⏟ ℎ𝑎𝑤𝑘 𝑘𝑒𝑦

Source: May 24, 2005 Minutes

Sentence Score: +1 (Hawkish) This adds one to the overall document score.

Example Sentence 2:

initial claims for unemployment⏟ 𝑑𝑜𝑣𝑒 𝑘𝑒𝑦

insurance declined⏟ 𝑛𝑒𝑔

further in recent weeks

Source: Aug. 20, 2014 Minutes

Sentence Score: +1 (Hawkish) This adds one to the overall document score.

Example Sentence 3:

outstanding residential mortgage debt declined⏟ 𝑛𝑒𝑔

further in the third quarter of 2010 reflecting

weak⏟ 𝑛𝑒𝑔

housing⏟ ℎ𝑎𝑤𝑘 𝑘𝑒𝑦

activity and tight lending standards

Source: Feb. 16, 2011 Minutes

Sentence Score: -1 (Dovish) This subtracts one from the overall document score.

43

Table 4: Descriptive Statistics of the Relative Z-score of FOMC Documents

Panel A: All Indices (December 2004 - December 2016 FOMC Meetings)

Statistic

Mean 2.88E-08

Standard Deviation 0.819

Minimum -2.305

Maximum 2.158

Minutes Release Days 89

Panel B: Indices covered by ETF data (December 2004 - March 2014 FOMC Meetings)

Statistic

Mean 0.033

Standard Deviation 0.86

Minimum -2.305

Maximum 2.158

Minutes Release Days 75

Panel C: Indices covered by ForEx data (April 2008 - December 2016 FOMC Meetings)

Statistic

Mean 0.006

Standard Deviation 0.814

Minimum -1.758

Maximum 2.158

Minutes Release Days 63

44

Table 5: MLE Regression Results

Dependent Variable:

Variables

0.368***

(0.09)

-0.296***

(0.073)

constant -0.01

(0.073)

Log-likelihood -91.228

Observations 88

Note: *** indicates significance at the 1% level, ** signifies

significance at the 5% level, and * indicates significance at

the 10% level. The numbers in parentheses are the robust

standard errors. The data period is Jan. 1, 2005 to

Jan. 12, 2016. The regression specification is

𝑅𝑍𝑡−1

𝑍𝑡𝑆

𝑅𝑍𝑡

𝑅𝑍𝑡 = 𝛾0 + 𝛾𝑅𝑍𝑡−1𝑅𝑍𝑡−1 + 𝛾𝑍𝑡𝑆𝑍𝑡𝑆 + 𝜉𝑡

45

Table 6: Descriptive Statistics of the News Shock of the Minutes

Panel A: All Indices (December 2004 - December 2016 FOMC Meetings)

Statistic

Mean -0.008

Standard Deviation 0.748

Minimum -2.407

Maximum 2.178

Minutes Release Days 88

Panel B: Indices covered by ETF data (December 2004 - March 2014 FOMC Meetings)

Statistic

Mean 0.014

Standard Deviation 0.784

Minimum -2.407

Maximum 2.178

Minutes Release Days 74

Panel C: Indices covered by ForEx data (April 2008 - December 2016 FOMC Meetings)

Statistic

Mean -0.006

Standard Deviation 0.725

Minimum -1.424

Maximum 1.647

Minutes Release Days 63

46

Tab

le7:

Basi

cR

egre

ssio

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esu

lts

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: B

asic

Reg

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Contr

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# o

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2358

1909

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2349

Pan

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BP

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1934

1934

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he

rob

ust

sta

nd

ard

erro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d f

or

ET

Fs

is D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4 w

hil

e th

e d

ate

per

iod

fo

r F

orE

x i

s

May

7,

20

08

to

Jan

12

, 2

01

6.

Th

e re

gre

ssio

n s

pec

ific

atio

n i

s

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

+Β𝑌𝑌+ℎ𝑡

𝑁𝑆 𝑡

47

Tab

le8:

Main

Regre

ssio

nR

esu

lts

for

ET

F’s

Vari

ab

les

SP

Y (

S&

P 5

00

ET

F)

VN

Q (

RE

IT E

TF

) E

EM

(E

ME

Eq

uit

y E

TF

)E

WJ (

Jap

an

Eq

uit

y E

TF

)

-10

-51.5

*-1

4.8

-4

(9.3

)(2

7.6

)(1

2.5

)(7

.2)

29.2

**

98.4

***

43.6

**

15.9

(14.1

)(3

3.1

)(1

8.9

)(1

3.7

)

Contr

ols

YE

SY

ES

YE

SY

ES

Yea

r F

EY

ES

YE

SY

ES

YE

S

R2

0.2

16

0.1

54

0.1

94

0.1

18

# o

f O

bs

2358

1909

2332

2349

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

sta

nd

ard

erro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4.

Th

e re

gre

ssio

n s

pec

ific

atio

n i

s

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽𝑙 2011∗𝑁

𝑆𝑙 2011∗𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

+𝛽𝑙 2011𝑙 2011+Β𝑌𝑌+ℎ𝑡

48

Tab

le9:

Main

Regre

ssio

nR

esu

lts

for

ForE

x

Vari

ab

les

U

SD

- J

ap

an

ese

Yen

U

SD

- S

wis

s F

ran

c G

BP

- U

SD

E

uro

- U

SD

-5.1

0.0

2-2

.5-5

.1

(5.1

)(3

.5)

(5.3

)(4

.8)

15.1

**

7.9

-0.1

-2.2

(7)

(6.5

)(7

.2)

(7.5

)

Contr

ols

YE

SY

ES

YE

SY

ES

Yea

r F

EY

ES

YE

SY

ES

YE

S

R2

0.0

39

0.0

09

0.0

32

0.0

24

# o

f O

bs

1934

1934

1934

1934

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

stan

dar

d e

rro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s M

ay 7

, 2

00

8 t

o J

an 1

2,

20

16

. T

he

regre

ssio

n s

pec

ific

atio

n i

s

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽𝑙 2011∗𝑁

𝑆𝑙 2011∗𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

+𝛽𝑙 2011𝑙 2011+Β𝑌𝑌+ℎ𝑡

𝑙 2011∗𝑁𝑆 𝑡

49

Tab

le10:

Regre

ssio

nR

esu

lts

for

Fed

Fu

nd

sFu

ture

s

Vari

ab

les

FF

1F

F3

FF

6F

F12

0.4

0.8

1.1

1.5

**

(0.4

)(0

.7)

(0.7

)(0

.7)

-0.4

-1-1

.7**

-3***

(0.4

)(0

.7)

(0.7

)(1

)

Contr

ols

YE

SY

ES

YE

SY

ES

Yea

r F

EY

ES

YE

SY

ES

YE

S

R2

0.0

35

0.0

62

0.0

68

0.0

57

# o

f O

bs

2599

2602

260

42607

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

stan

dar

d e

rro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

5.

Th

e re

gre

ssio

n s

pec

ific

atio

n i

s

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽𝑙 2011∗𝑁

𝑆l 2011∗𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

+𝛽𝑙 2011l 2011+Β𝑌𝑌+𝜔𝑡

l 2011∗𝑁𝑆 𝑡

50

Table 11: MLE Regression Results for the Expected Component of RZHt and RZDt

Variables

0.271*** 0.419***

(0.093) (0.084)

-0.352*** -0.403***

(0.084) (0.078)

constant -0.013 0.008

(0.081) (0.078)

Log-likelihood -101.085 -97.291

Observations 88 88

Note: *** indicates significance at the 1% level, ** signifies significance at the 5%

level, and * indicates significance at the 10% level. The numbers in parentheses are

the robust standard errors. The data period is Jan. 1, 2005 to Jan. 12, 2016. The

regression specification is

𝑅𝑍𝑡−1𝑔

𝑍𝑡,𝑆𝑔

𝑹𝒁𝒕𝑯

𝑅𝑍𝑡𝑔= 𝛾0 + 𝛾𝑅𝑍𝑡−1

𝑔 𝑅𝑍𝑡−1𝑔

+ 𝛾𝑍𝑡,𝑆𝑔 𝑍𝑡,𝑆

𝑔+ 𝜉𝑡

𝑹𝒁𝒕𝑫

51

Table 12: Descriptive Statistics of NSHt and NSDt

Panel A: All Indices (December 2004 - December 2016 FOMC Meetings)

Statistic

Mean -0.011 0.004

Standard Deviation 0.846 0.84

Minimum -2.32 -2.297

Maximum 2.473 2.232

Minutes Release Days 88 88

Panel B: Indices covered by ETF data (December 2004 - March 2014 FOMC Meetings)

Statistic

Mean -0.005 -0.031

Standard Deviation 0.889 0.887

Minimum -2.32 -2.297

Maximum 2.473 2.232

Minutes Release Days 74 74

Panel C: Indices covered by ForEx data (April 2008 - December 2016 FOMC Meetings)

Statistic

Mean -0.033 -0.012

Standard Deviation 0.737 0.871

Minimum -1.564 -2.297

Maximum 1.929 2.159

Minutes Release Days 63 63

𝑵𝑺𝒕𝑯

𝑵𝑺𝒕𝑯

𝑵𝑺𝒕𝑯

𝑵𝑺𝒕𝑫

𝑵𝑺𝒕𝑫

𝑵𝑺𝒕𝑫

52

Tab

le13:

Regre

ssio

nR

esu

lts:

Separa

ted

Haw

kan

dD

ove

Senti

ments

Va

ria

ble

sS

PY

(S

&P

500 E

TF

)V

NQ

(R

EIT

ET

F)

EE

M (

EM

E E

qu

ity

ET

F)

EW

J (

Ja

pa

n E

qu

ity E

TF

)

-1.4

-10

-3.3

-2.8

(6.7

)(1

7.9

)(9

)(6

.3)

94

211

.80.9

(11.2

)(3

0.5

)(1

5.2

)(8

.4)

29

.6*

**

52

.8*

*27

.92

9.4

*

(11.5

)(2

4.1

)(1

8.9

)(1

6.1

)

-3.9

-52

.3-1

9.8

9.3

(14.3

)(3

5.1

)(2

0.4

)(1

2.4

)

Co

ntr

ols

YE

SY

ES

YE

SY

ES

Yea

r F

EY

ES

YE

SY

ES

YE

S

R2

0.2

17

0.1

54

0.1

94

0.1

19

# o

f O

bs

2358

19

09

23

32

23

49

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

sta

nd

ard

erro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4.

Th

e re

gre

ssio

n s

pec

ific

atio

n i

s

l 2011∗𝑁𝑆 𝑡𝐻

l 2011∗𝑁𝑆 𝑡𝐷

𝑁𝑆 𝑡𝐷

𝑁𝑆 𝑡𝐻

𝑟 𝑡𝑓=𝑣+𝛽𝑁𝑆𝐻𝑁𝑆 𝑡𝐻+𝛽𝑁𝑆𝐷𝑁𝑆 𝑡𝐷+𝛽𝑙 2011∗𝑁

𝑆𝐻l 2011∗𝑁𝑆 𝑡𝐻+𝛽𝑙 2011∗𝑁

𝑆𝐷l 2011∗𝑁𝑆 𝑡𝐷+𝛽𝑙 2011l 2011+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

𝑡+𝛽𝑌𝑌+𝜓𝑡

53

Tab

le14:

Regre

ssio

nR

esu

lts:

Accou

nti

ng

for

ZL

B

Pan

el A

: Re

gres

sio

n R

esu

lts

for

SPY

and

VN

Q

Va

ria

ble

s

S

PY

(S

&P

500 E

TF

)

VN

Q (

RE

IT E

TF

)

[1]

[2]

[3]

[4]

-10

-16.1

-51.5

*-6

7.4

(9.3

)(1

3.6

)(2

7.6

)(5

0)

29.2

**

98

.4*

**

(14.1

)(3

3.1

)

24

.16

8.2

(15

.8)

(53

.8)

Co

ntr

ols

YE

SY

ES

YE

SY

ES

Year

FE

YE

SY

ES

YE

SY

ES

R2

0.2

16

0.2

16

0.1

54

0.1

56

# o

f O

bs

235

8235

819

09

19

09

Pan

el B

: Re

gres

sio

n R

esu

lts

for

EEM

an

d E

WJ

Va

ria

ble

s

E

EM

(E

ME

Eq

uit

y E

TF

)

E

WJ

(Ja

pa

n E

qu

ity

ET

F)

[1]

[2]

[3]

[4]

-14

.8-2

6.1

-4-1

1.6

(12.5

)(1

7.9

)(7

.2)

(9.3

)

43.6

**

15

.9

(18.9

)(1

3.7

)

40.6

**

21

.6*

(20

.7)

(11

.8)

Co

ntr

ols

YE

SY

ES

YE

SY

ES

Year

FE

YE

SY

ES

YE

SY

ES

R2

0.1

94

0.1

94

0.1

18

0.1

2

# o

f O

bs

233

2233

223

49

23

49

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

sta

nd

ard

erro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4.

Th

e re

gre

ssio

n s

pec

ific

atio

n w

ith

ZL

B i

s

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽ZLB∗𝑁

𝑆𝑍𝐿𝐵∗𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

+𝛽ZLB𝑍𝐿𝐵+Β𝑌𝑌+𝑗 𝑡

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

𝑍𝐿𝐵∗𝑁𝑆 𝑡

𝑍𝐿𝐵∗𝑁𝑆 𝑡

54

Tab

le15:

Regre

ssio

nR

esu

lts:

Ch

an

gin

gth

eD

ate

Cu

toff

Pan

el A

: Re

gres

sio

n R

esu

lts

for

SPY

and

VN

Q

Va

ria

ble

s

S

PY

(S

&P

500 E

TF

)

VN

Q (

RE

IT E

TF

)

[1]

[2]

[3]

[4]

-10

-9.2

-51

.5*

-50

.3*

(9.3

)(9

.2)

(27

.6)

(26

.5)

29

.2*

*9

8.4

***

(14

.1)

(33

.1)

29

.4*

*1

07

.2*

**

(12

.4)

(29

.8)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

R2

0.2

16

0.2

16

0.1

54

0.1

54

# o

f O

bs

23

58

23

58

19

09

19

09

Pan

el B

: Re

gres

sio

n R

esu

lts

for

EEM

an

d E

WJ

Va

ria

ble

s

E

EM

(E

ME

Eq

uit

y E

TF

)

E

WJ

(Ja

pa

n E

qu

ity

ET

F)

[1]

[2]

[3]

[4]

-14

.8-1

2.5

-4-2

.6

(12

.5)

(12

.3)

(7.2

)(7

.1)

43

.6*

*1

5.9

(18

.9)

(13

.7)

40

**1

3

(17

.8)

(14

.1)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

R2

0.1

94

0.1

94

0.1

18

0.1

18

# o

f O

bs

23

32

23

32

23

49

23

49

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

sta

nd

ard

erro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4.

Th

e re

gre

ssio

n s

pec

ific

atio

n u

sed

is

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽𝑝𝑜𝑠𝑡2011∗𝑁

𝑆𝑝𝑜𝑠𝑡2011∗𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

+𝛽𝑝𝑜𝑠𝑡2011𝑝𝑜𝑠𝑡2011+Β𝑌𝑌+𝑗 𝑡

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

𝑝𝑜𝑠𝑡2011∗𝑁𝑆 𝑡

𝑝𝑜𝑠𝑡2011∗𝑁𝑆 𝑡

55

Tab

le16:

Regre

ssio

nR

esu

lts:

Retu

rns

on

Min

ute

sD

ays

Pan

el A

: Re

gres

sio

n R

esu

lts

for

SPY

and

VN

Q

Va

ria

ble

s

S

PY

(S

&P

500 E

TF

)

VN

Q (

RE

IT E

TF

)

[1]

[2]

[3]

[4]

-10

-7.9

-51

.5*

-57

**

(9.3

)(9

.5)

(27

.6)

(27

.6)

29

.2*

*3

5.7

**

98

.4*

**1

19

.4*

*

(14

.1)

(17

.6)

(33

.1)

(48

.9)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

R2

0.2

16

0.4

93

0.1

54

0.4

15

# o

f O

bs

23

58

74

19

09

61

Pan

el B

: Re

gres

sio

n R

esu

lts

for

EEM

an

d E

WJ

Va

ria

ble

s

E

EM

(E

ME

Eq

uit

y E

TF

)

E

WJ

(Ja

pa

n E

qu

ity

ET

F)

[1]

[2]

[3]

[4]

-14

.8-1

4.2

-4-2

.2

(12

.5)

(12

)(7

.2)

(7.5

)

43

.6*

*4

8.3

*1

5.9

13

.3

(18

.9)

(25

.1)

(13

.7)

(15

.2)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

R2

0.1

94

0.4

59

0.1

18

0.3

83

# o

f O

bs

23

32

73

23

49

74

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

sta

nd

ard

erro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4.

Th

e re

gre

ssio

n s

pec

ific

atio

n i

s

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽𝑙 2011∗𝑁

𝑆𝑙 2011∗𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

𝑡+𝛽𝑙 2011𝑙 2011+Β𝑌𝑌+𝜙𝑡

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

56

Tab

le17:

Regre

ssio

nR

esu

lts:

Usi

ng

Resi

du

als

as

New

sS

hock

Pan

el A

: Re

gres

sio

n R

esu

lts

for

SPY

and

VN

Q

Va

ria

ble

s

S

PY

(S

&P

500 E

TF

)

VN

Q (

RE

IT E

TF

)

[1]

[2]

[3]

[4]

2.4

-5.4

-53

.8*

-53

.8

(6.8

)(8

.5)

(28

.6)

(32

.5)

52

**7

6**

16

8.9

***

21

3.1

**

(23

.4)

(32

.7)

(45

.1)

(83

.9)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

R2

0.2

16

0.4

94

0.1

54

0.3

81

# o

f O

bs

23

58

74

19

09

61

Pan

el B

: Re

gres

sio

n R

esu

lts

for

EEM

an

d E

WJ

Va

ria

ble

s

E

EM

(E

ME

Eq

uit

y E

TF

)

E

WJ

(Ja

pa

n E

qu

ity

ET

F)

[1]

[2]

[3]

[4]

0.6

-10

.61

0.1

1.7

(11

.7)

(11

.2)

(8.8

)(8

.4)

80

.6*

*1

06

.6*

*3

3.9

32

(32

.3)

(50

.4)

(28

.9)

(30

.7)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

R2

0.1

94

0.4

61

0.1

19

0.3

86

# o

f O

bs

23

32

73

23

49

74

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

rob

ust

sta

nd

ard

erro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4.

Th

e re

gre

ssio

n s

pec

ific

atio

n i

s

l 2011∗𝜉 𝑡

𝜉 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝜉𝜉 𝑡+𝛽𝑙 2011∗𝜉𝑙 2011∗𝜉 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

𝑡+𝛽𝑙 2011𝑙 2011+Β𝑌𝑌+𝜙𝑡

l 2011∗𝜉 𝑡

𝜉 𝑡

57

Tab

le18:

Regre

ssio

nR

esu

lts:

Boots

trap

Sta

nd

ard

Err

ors

Pan

el A

: Re

gres

sio

n R

esu

lts

for

SPY

and

VN

Q

Va

ria

ble

s

S

PY

(S

&P

500 E

TF

)

VN

Q (

RE

IT E

TF

)

[1]

[2]

[3]

[4]

-7.9

-7.9

-57

**-5

7*

(9.5

)(1

0.6

)(2

7.6

)(2

9.9

)

35

.7*

*3

5.7

*1

19

.4*

*1

19

.4*

*

(17

.6)

(20

.5)

(48

.9)

(52

.3)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

Rep

licat

ion

sn

/a9

,02

4n

/a8

,92

2

# o

f O

bs

74

74

61

61

Pan

el B

: Re

gres

sio

n R

esu

lts

for

EEM

an

d E

WJ

Va

ria

ble

s

E

EM

(E

ME

Eq

uit

y E

TF

)

E

WJ

(Ja

pa

n E

qu

ity

ET

F)

[1]

[2]

[3]

[4]

-14

.2-1

4.3

-2.2

-2.2

(12

)(1

3.4

)(7

.5)

(8.3

)

48

.3*

48

.3*

13

.31

3.3

(25

.1)

(28

.7)

(15

.2)

(19

.2)

Co

ntr

ols

YES

YES

YES

YES

Year

FE

YES

YES

YES

YES

Rep

licat

ion

sn

/a9

,05

4n

/a8

,98

8

# o

f O

bs

73

73

74

74

No

te:

*** i

nd

icat

es s

ign

ific

ance

at

the

1%

lev

el,

** s

ign

ifie

s si

gn

ific

ance

at

the

5%

lev

el,

and

* i

nd

icat

es s

ign

ific

ance

at

the

10

% l

evel

. T

he

nu

mb

ers

in p

aren

thes

es a

re t

he

stan

dar

d e

rro

rs.

Th

e u

nit

s fo

r co

effi

cien

t es

tim

ates

an

d t

hei

r st

and

ard

dev

iati

on

s ar

e b

asis

po

ints

. T

he

dat

a p

erio

d i

s D

ec.

1,

20

04

to

Ap

ril

30

, 2

01

4.

Th

e re

gre

ssio

n s

pec

ific

atio

n i

s

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

𝑟 𝑡𝑓=𝛼+𝛽𝑁𝑆𝑁𝑆 𝑡+𝛽𝑙 2011∗𝑁

𝑆𝑙 2011∗𝑁𝑆 𝑡+𝛽𝑉𝐼𝑋𝑉𝐼𝑋

𝑡+𝛽𝑙 2011𝑙 2011+Β𝑌𝑌+𝜙𝑡

l 2011∗𝑁𝑆 𝑡

𝑁𝑆 𝑡

58